active Phase 4 ยท Analytics & Optimization · 0 log entries

ggTrader

An algorithmic trading bot and a research framework for professional strategy development.

ggTrader

Objective

Create a reproducible and scalable trading research and execution framework for professional traders.

Project Overview

ggTrader is a professional algorithmic trading framework designed for high-performance research and execution. It moves away from monolithic scripts toward a modular, scalable architecture that supports complex strategy validation and multi-exchange connectivity.

Roadmap & Development Phases

The framework progresses through the following research and execution milestones:

Phase 4 In Progress · 7 Development Milestones · Quant Strategy Track
Phase 1 Complete

Foundation & Core Engine

Built the modular trading engine and reproducible simulation logic using VectorBT patterns.

Phase 2 Complete

Data Ingestion & Exchange Adapters

Kraken REST/WebSocket exchange adapters and high-speed partitioned Parquet data stores.

Phase 3 Complete

Optimization & Walk-Forward Testing

Implemented Walk-Forward Optimization (WFO) and multi-dimensional parameter sensitivity analysis.

Phase 4 · Active Target Active

Interactive Analytics & Audit Trails

Deep Jupyter Notebook visual tooling and ResultsManager timestamped run archives.

Phase 5 Planned

Execution & Risk Architecture

Dynamic position sizing algorithms, drawdown guards, and order-book impact simulations.

Phase 6 Planned

Live Paper Validation

Real-time shadow execution against live order books with execution slippage tracking.

Phase 7 Stretch

Production Cloud Deployment

Containerized multi-strategy execution nodes with automated failover and telemetry pings.

Core Features

  • Modular Architecture: Clean separation between core engine logic, portfolio management, signal indicators, and exchange adapters (Kraken).
  • Reproducible Research: A dedicated ResultsManager and timestamped results folders ensure that every backtest and optimization run is tracked and auditable.
  • Walk-Forward Optimization (WFO): Finds stable parameters over sliding time windows to reduce overfitting.
  • Sensitivity Analysis: Tests how strategy performance reacts to parameter drift, ensuring robustness in changing markets.
  • Professional Analytics: Deep integration with Jupyter Notebooks for interactive visualization and performance deep-dives.

Technical Architecture

  • Core: Python, NumPy, Pandas
  • Backtesting: High-performance simulation logic (integrated with VectorBT patterns)
  • Data: Parquet local storage, Kraken API adapters
  • Optimization: Custom WFO and sensitivity analysis pipelines

Project Log

No log entries found for this project.

← All projects